Many modern vehicles are equipped with advanced safety and driver-assist systems that require robust and precise object detection and tracking systems to control responsive host vehicle maneuvers. These systems utilize periodic or continuous detection of objects and control algorithms to estimate various object parameters, such as the relative object range, range rate (i.e., closing or opening velocity of object), direction of travel, object position, and size of the object. The object detection systems may use any of a number of detection technologies, such as radar, vision imaging, laser, light detection and ranging (LiDAR), ultrasound, etc. Each of these detection systems contribute to object detection and to estimating object parameters in different ways, and with various limitations. Detecting generally long objects in particular can be challenging due to performance limitations associated with some detection systems.
For example, radar devices detect and locate objects by transmitting electromagnetic signals that reflect off objects within a sensor's field-of-view. The reflected signal returns to the radar as an echo where it is processed to determine various information such as the round-trip travel time of the transmitted/received energy. The round trip travel time is directly proportional to the range of the object from the radar. In addition to range determination, there are methods to determine azimuth (i.e. cross-range) location of detected objects. Therefore, depending on its complexity, radars are capable of locating objects in both range and azimuth relative to the device location.
Based on the reflected signals during a sampling of the entire sensor field-of-view, radar devices accumulate a set of detection points. Due to the nature of “reflections” collected by a remote sensor (whether a radar, laser, ultrasonic, or other active sensor), the set of detection points is representative of only certain spots on the object or objects present in the sensor's field-of-view. These detection points are analyzed in order to determine what type of objects may be present and where such object(s) are located. However, short-range radar devices lack the angular and spatial resolution necessary to discern object-identifying details and to distinguish between closely-located objects (i.e., no point target assumption). Performance degradation also arises in radar systems when there is little or no relative speed between the host and the object, making it difficult to estimate speed and direction. With respect to detecting long objects in particular, since the reflected measurement signals can vary significantly at different locations for the same object, radar devices are unable to directly group or cluster detection points effectively.
Vision imaging is also widely used by object detection and tracking systems to identify and classify objects located proximate to the host vehicle. In general, vision systems capture images with one or more camera(s), and extract objects and features from the images using various image processing techniques. The object is then tracked between the images as the object moves within the host vehicle's field-of-view. However, detecting long objects by vision is still very challenging especially when the object is too long and across the whole image frame for which the vision algorithms tend to split the long object into multiple short objects.
LiDAR sensors measure range using a time of flight principle. A light pulse is emitted for a defined length of time, reflected off a target object, and received via the same path (line-of-sight) along which it was sent. Because light travels with constant velocity, the time interval between emission and detection is proportional to a distance between the sensor to the point of reflection. However, it is difficult to estimate target speed using LiDAR because there is no direct speed measurement from the sensors.